The approaches described in this section could be pursued, but are not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
Nowadays, a lot of available services are offered to users on different mobile terminals such as tablets and smartphones. This proliferation of services resulted in the apparition of assistance tools that help the users for organizing their digital things, e.g. media content, documents, Internet of Things (IoT), TV programs, etc. These tools are known as Personal Assistants or Personal Agents. Their purpose is to propose to end users helpful and personalized activities to do at the right time and the right location.
Commercial examples of these personalized assistants are Google Now (http://en.wikipedia.org/wiki/Google_Now) or Siri (http://en.wikipedia.org/wiki/Siri_(software)). They are based on context awareness and more particularly on the geolocalization of the end user, generally using GPS (Global Positioning System) and Wifi services which are the most popular features available on a smartphone.
However, although the location results obtained by GPS are better than those obtained by Wifi, GPS localization accuracy does not exceed 10 to 20 meters. Besides, it necessitates a prohibitive acquisition time of the location coordinates that can reach several seconds, thus increasing the response time of the Personal Assistant service and degrading the end user Quality of Experience (QoE). Moreover, GPS localization is generally not operational in an indoor environment, as it requires satellite signals reception. As a consequence, GPS is not able to distinguish between rooms or floors, for instance.
Existing alternative methods to detect a user presence use human face or voice recognition. However, the involved digital processing is too complex to be performed in a smartphone. Besides, the localization results are subject to errors due, for example, to ambient noise, low light intensity, different voice tone, etc.
The document US2012/0083285 describes a method for obtaining enhanced location information for a mobile device combining the use of a GPS receiver and of additional data providing context information for the mobile device.
However, the method disclosed in this document does not permit accurate identification of the environment in which the mobile device is located. For instance, this method is not able to detect if the mobile device is in the user's home or in a friend's home.